Method for diagnosing heart disease, predicting sudden death, and analyzing treatment response using multifractal analysis

Abstract

A method of analyzing electrocardiogram (EKG) data for use in the diagnosis of heart disease, prognosis of cardiac conditions, and the monitoring of heart disease therapies is disclosed. The method utilizes a wavelet-based multifractal analysis with one or more of (1) a discrete wavelet smoothing step to remove the effects of abnormal beats; (2) “Levy flight” analysis to detect the frequency of abnormal beats known to adversely affect the multifractal (MF) analysis; and (3) MF alpha analysis, a multifractal extension of monofractal short term (ST) alpha analysis. The invention further comprises an EKG test battery comprising Levy flight anomalous beat/beat cluster screening, followed by (ST) MF alpha analysis and MF Holder analysis (when validated by the Levy flight analysis). The wavelet smoothing step can also be used to classify human EKGs by observing the effect of sequential smoothing on the MF Holder coefficient. Alternative choices to the wavelet smoothing approach to removal of abnormal beat effects include probability distribution function analysis to determine the MF Holder coefficient directly, abnormal beat ridge skeleton removal to remove the offending beats based on a direct multifractal spectrum calculation, and the calculation of various types of entropy coefficients for the EKG time series.

Description

BACKGROUND OF THE INVENTION[0001]The present invention is directed to a battery of multifractal-based tests developed for the analysis of electrocardiogram (EKG) data. The invention is preferably for clinical use in a novel integrated approach to diagnosis of heart disease, prognosis of cardiac conditions, and monitoring of heart disease therapies. This multifractal approach is not available with current, clinically available cardiographic methods.[0002]Cardiovascular disease currently affects approximately 20 million Americans. Roughly 12 million Americans are affected by coronary artery disease (CAD), and 5 million suffer from congestive heart failure (CHF). More importantly, millions suffer from undiagnosed heart disease; the prevalence of undiagnosed CHF is estimated to be approximately 20 million in the U.S. In addition, 400,000 Americans with CAD or CHF die from sudden cardiac death each year. Although there are a number of conventional nonfractal tests for the diagnosis of he...

AI Technical Summary

Benefits of technology

[0020]Fourth, the application of our multifractal analysis has the unexpected side benefit of reducing the minimum size of the EKG time series snapshot required for fractal analysis. This may be due to the increased analytic detail offered by the multifractal analysis. In a preferred embodiment, the snapshot size may be readily reduced to 2048 beats without increase in statistical uncertainty when compared to other less sophisticated (monofractal) methods, where 4098 beats or more may be necessary. We can overlap multifractal snapshots as much as 16 times with an additional reduction in sampling error by a factor of two or more. This new improved analysis leads to useful time series information on a EKG time series of only 2 hours in length, with time resolution of less than 15 minutes even with a 2048-beat snapshot. Thus our MF Holder technique can now routinely examine the time series dependence of a variety of MF parameters in an EKG recording, permitting comparison with time of day, current heart rate, time of drug therapy, or any other measurable parameter, such as PO2, cardiac output, or blood pressure. This greatly increases the utility of this invention in screening/treatment of heart disease in a clinical setting.

[0021]Fifth, certain embodiments of the present invention comprise the development, validity testing, and application of our ST multifractal alpha coefficient (MF alpha). We applied MF mathematical methodology to the 1-4 beat scaling region we found optimal after our monofractal alpha scaling analysis, which was based on the same CWT wavelet analysis of the EKG RR-interval snapshots we used with our MF Holder analysis. The MF alpha method is altered in that the MF analysis is ignored for negative values of q, and that the “sup” method used in the MF Holder analysis is not needed. A MF alpha cascade is not defined due to the limitations of the method. We determined that the MF Holder coefficient for q=2 is the MF alpha analogue, and validated our new MF alpha by comparison with our old monofractal alpha, analyzing all 47 (32 CHF and 15 CAD) patients

Problems solved by technology

This high percentage of abnormal beats is t

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